Contour Detection and Hierarchical Image Segmentat
2016-08-23
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This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We
present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization
framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any
contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour
detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly
outperform competing algorithms. The automatically generated hierarchical segmentations ca
present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization
framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any
contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour
detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly
outperform competing algorithms. The automatically generated hierarchical segmentations ca
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